


import argparse
import os

import torch

from transformers.utils import WEIGHTS_NAME


__UpperCamelCase     		=							['small', 'medium', 'large']

__UpperCamelCase     		=							'lm_head.decoder.weight'
__UpperCamelCase     		=							'lm_head.weight'
def 							UpperCamelCase_(					_A :str					,  _A :str	)->		Union[str, Any]:
			UpperCamelCase__       =							torch.load(_A	)
			UpperCamelCase__       =							d.pop(_A	)
			os.makedirs(_A					,  exist_ok=_A	)
			torch.save(_A					,  os.path.join(_A					,  _A	)	)


if __name__ == "__main__":
						__UpperCamelCase     		=							argparse.ArgumentParser()
						parser.add_argument('--dialogpt_path', default='.', type=str)
						__UpperCamelCase     		=							parser.parse_args()
						for MODEL in DIALOGPT_MODELS:
												__UpperCamelCase     		=							os.path.join(args.dialogpt_path, f'''{MODEL}_ft.pkl''')
												__UpperCamelCase     		=							f'''./DialoGPT-{MODEL}'''
												convert_dialogpt_checkpoint(
												    checkpoint_path,
												    pytorch_dump_folder_path,
												)






# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers import DiffusionPipeline, ImagePipelineOutput







class 							lowerCamelCase__						(				UpperCAmelCase   ):






			"""simple docstring"""






			def __init__(				self   ,    snake_case   ,    snake_case  ):

						'''simple docstring'''


						super().__init__()
						self.register_modules(unet=snake_case   ,    scheduler=snake_case  )







			@torch.no_grad()
			def __call__(				self   ,    snake_case = 1   ,    snake_case = None   ,    snake_case = 50   ,    snake_case = "pil"   ,    snake_case = True   ,    **snake_case   ,    ):

						'''simple docstring'''


						UpperCamelCase__       =							torch.randn(
						    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)   ,    generator=snake_case   ,    )
						UpperCamelCase__       =							image.to(self.device  )

						# set step values
						self.scheduler.set_timesteps(snake_case  )

						for t in self.progress_bar(self.scheduler.timesteps  ):
									# 1. predict noise model_output
									UpperCamelCase__       =							self.unet(snake_case   ,    snake_case  ).sample

									# 2. predict previous mean of image x_t-1 and add variance depending on eta
									# eta corresponds to η in paper and should be between [0, 1]
									# do x_t -> x_t-1
									UpperCamelCase__       =							self.scheduler.step(snake_case   ,    snake_case   ,    snake_case  ).prev_sample

						UpperCamelCase__       =							(image / 2 + 0.5).clamp(0   ,    1  )
						UpperCamelCase__       =							image.cpu().permute(0   ,    2   ,    3   ,    1  ).numpy()
						if output_type == "pil":
									UpperCamelCase__       =							self.numpy_to_pil(snake_case  )

						if not return_dict:
									return (image,), "This is a local test"

						return ImagePipelineOutput(images=snake_case  ), "This is a local test"


